Rear Admiral Daniel Hagari of the Israeli Defense Forces updates the public Saturday, April 13 on Iranian UAVs being launched toward the Jewish state. Iran launched dozens of unmanned aerial vehicles (UAV), also known as drones, at Israel on Saturday evening local time after a week of threatening retaliation for an attack on a consulate in Damascus. "Iran has begun an airborne attack against Israel," White House National Security Council spokesperson Adrienne Watson said in a statement Saturday. "President Biden is being regularly updated on the situation by his national security team and will meet with them this afternoon at the White House." Iran's state-run news agency Mehr News reported that the Islamic Revolutionary Guard Corps (IRGC) had announced the start of the "anti-Zionist operation," which would hit targets in the Palestinian territories, with further details of the operation announced soon.

Rep. Carlos Gimenez, R-Fla., tells'Fox News Live' that China, Russia, North Korea and Iran want to establish a'new world order.' Lawmakers reacted after Iran launched drones from its own territory toward Israel late Saturday, calling for the White House to "stand firm" and "stop coddling Iran." Speaker Mike Johnson pledged America's "full resolve" to stand with Israel. "As Israel faces this vicious attack from Iran, America must show our full resolve to stand with our critical ally," Johnson said in a statement. "The world must be assured: Israel is not alone."

The White House vowed Saturday that the United States' support for Israel's security is "ironclad," pledging to stand with the Jewish state and "support their defense" after Iran launched an aerial drone attack towards the country Saturday afternoon. Iran launched drones from its own territory toward Israel late Saturday, days after its Supreme Leader warned it would hit back in response to an airstrike on the Iranian consulate in Syria that left several generals dead. "Iran has begun an airborne attack against Israel," White House National Security Council spokesperson Adrienne Watson said in a statement Saturday. "President Biden is being regularly updated on the situation by his national security team and will meet with them this afternoon at the White House." The White House said the president's team "is in constant communication with Israeli officials as well as other partners and allies."

After months of delays, the US House of Representatives voted on Friday to extend a controversial warrantless wiretap program for two years. Known as Section 702, the program authorizes the US government to collect the communications of foreigners overseas. But this collection also includes reams of communications from US citizens, which are stored for years and can later be warrantlessly accessed by the FBI, which has heavily abused the program. An amendment that would require investigators to obtain such a warrant failed to pass. A group of US lawmakers on Sunday unveiled a proposal that they hope will become the country's first nationwide privacy law.

Climate risk assessment is becoming increasingly important. For organisations, identifying and assessing climate-related risks is challenging, as they can come from multiple sources. This study identifies and assesses the main climate transition risks in the colombian processed food sector. As transition risks are vague, our approach uses Fuzzy Logic and compares it to various multi-criteria decision-making methods to classify the different climate transition risks an organisation may be exposed to. This approach allows us to use linguistic expressions for risk analysis and to better describe risks and their consequences. The results show that the risks ranked as the most critical for this organisation in their order were price volatility and raw materials availability, the change to less carbon-intensive production or consumption patterns, the increase in carbon taxes and technological change, and the associated development or implementation costs. These risks show a critical risk level, which implies that they are the most significant risks for the organisation in the case study. These results highlight the importance of investments needed to meet regulatory requirements, which are the main drivers for organisations at the financial level.

We introduce HyperCLOVA X, a family of large language models (LLMs) tailored to the Korean language and culture, along with competitive capabilities in English, math, and coding. HyperCLOVA X was trained on a balanced mix of Korean, English, and code data, followed by instruction-tuning with high-quality human-annotated datasets while abiding by strict safety guidelines reflecting our commitment to responsible AI. The model is evaluated across various benchmarks, including comprehensive reasoning, knowledge, commonsense, factuality, coding, math, chatting, instruction-following, and harmlessness, in both Korean and English. HyperCLOVA X exhibits strong reasoning capabilities in Korean backed by a deep understanding of the language and cultural nuances. Further analysis of the inherent bilingual nature and its extension to multilingualism highlights the model's cross-lingual proficiency and strong generalization ability to untargeted languages, including machine translation between several language pairs and cross-lingual inference tasks. We believe that HyperCLOVA X can provide helpful guidance for regions or countries in developing their sovereign LLMs.

M&A activities are pivotal for market consolidation, enabling firms to augment market power through strategic complementarities. Existing research often overlooks the peer effect, the mutual influence of M&A behaviors among firms, and fails to capture complex interdependencies within industry networks. Common approaches suffer from reliance on ad-hoc feature engineering, data truncation leading to significant information loss, reduced predictive accuracy, and challenges in real-world application. Additionally, the rarity of M&A events necessitates data rebalancing in conventional models, introducing bias and undermining prediction reliability. We propose an innovative M&A predictive model utilizing the Temporal Dynamic Industry Network (TDIN), leveraging temporal point processes and deep learning to adeptly capture industry-wide M&A dynamics. This model facilitates accurate, detailed deal-level predictions without arbitrary data manipulation or rebalancing, demonstrated through superior evaluation results from M&A cases between January 1997 and December 2020. Our approach marks a significant improvement over traditional models by providing detailed insights into M&A activities and strategic recommendations for specific firms.

Event coreference resolution (ECR) is the task of determining whether distinct mentions of events within a multi-document corpus are actually linked to the same underlying occurrence. Images of the events can help facilitate resolution when language is ambiguous. Here, we propose a multimodal cross-document event coreference resolution method that integrates visual and textual cues with a simple linear map between vision and language models. As existing ECR benchmark datasets rarely provide images for all event mentions, we augment the popular ECB+ dataset with event-centric images scraped from the internet and generated using image diffusion models. We establish three methods that incorporate images and text for coreference: 1) a standard fused model with finetuning, 2) a novel linear mapping method without finetuning and 3) an ensembling approach based on splitting mention pairs by semantic and discourse-level difficulty. We evaluate on 2 datasets: the augmented ECB+, and AIDA Phase 1. Our ensemble systems using cross-modal linear mapping establish an upper limit (91.9 CoNLL F1) on ECB+ ECR performance given the preprocessing assumptions used, and establish a novel baseline on AIDA Phase 1. Our results demonstrate the utility of multimodal information in ECR for certain challenging coreference problems, and highlight a need for more multimodal resources in the coreference resolution space.

While deep neural networks (DNNs) have led to remarkable results in standard supervised learning tasks in computer vision and natural language processing, they are widely recognized to be susceptible to minor adversarially-designed perturbations to their input data commonly regarded as adversarial attacks [1, 2]. Adversarial examples are typically designed by finding the worst-case norm-constrained perturbation that leads to the maximum impact on the classification loss at an input data point. To combat norm-bounded adversarial attacks, adversarial training (AT) methods [3] which learn a DNN classifier using adversarially-perturbed training examples have been shown to significantly improve the robustness of a DNN against norm-bounded adversarial attacks. Several variants of AT methods have been developed in the machine learning community to accelerate and facilitate the application of AT algorithms to large-scale machine learning problems [4, 5]. While AT algorithms have achieved state-of-the-art robustness scores against standard norm-bounded adversarial attacks, the generalization gap between their performance on training and test data has been frequently observed to be significantly greater than the generalization error of DNNs learned by standard empirical risk minimization (ERM) [6, 7]. To understand the significant generalization gap in adversarial training, several theoretical and empirical studies have focused on the generalization properties of adversariallytrained models [8,9]. These studies have attempted to analyze the generalization error in learning adversariallyrobust models and reduce the generalization gap by applying explicit and implicit regularization techniques such as early stopping and Lipschitz regularization methods. Specifically, several recent works [10-12] have focused on the connections between the optimization and generalization behavior of adversarially-trained models.

We live in frightening times. Wars in Ukraine and Gaza could widen, the polar ice caps are melting, and even some scientists developing artificial intelligence systems are worried about unleashing a monster. But those fears all pale in comparison to what really gives Americans the jitters. The calamity that worries them above all else is a total economic collapse in the US. The Pentagon's four legged robot dogs may offer a glimpse of what killer machines will look like An economic meltdown is the top fear for a third of respondents.